Overview of PCI's software capability for working with SAR imagery

1. Synthetic Aperture Radar (SAR)
PCI expertise and capabilities
January 2013

2. 70 + Employees
> 25,000
licenses
installed
worldwide
HQ: Toronto Awards & Accolades
Offices in: Innovation Awards
Gatineau, for
60 Resellers
USA, China
Worldwide GXL
Geomatica

3. WHERE DOES
PCI GEOMATICS
FIT

4. GEOSPATIAL VALUE CHAIN
Image Image Pre- Image Tools & Value-Added
Collection Processing Processing Work Flow Content
Selected Capabilities
Satellite Orthorectification Image Extraction Display , Storage and Google Maps/Earth
Dissemination
SAR (Radar) Atmospheric Spatial Analysis Microsoft Bing Maps
correction Ingestion Tools
LIDAR Image Classification Location Based Services
Image Mosaiking Enterprise Integration
Airborne Camera Customized Vertical Applications –
Pan Sharpening Algorithms Open Source natural resource,
Other Image Sensing
Development weather, land planning,
etc.
Selected Competitors/Partners
Digital Globe PCI Geomatics PCI Geomatics PCI Geomatics Google / Yahoo / Microsoft
Vexcel / Microsoft ERDAS (Leica) Definiens AG ESRI / Intergraph ESRI / Intergraph
GeoEye ENVI (ITT) ERDAS (Leica) Pixel Factory Vertical Applications (e.g.,
(InfoTerra) RapidEye for Agriculture and
ENVI (ITT)
Iunctus for Oil and Gas)
Page 4

5. WHAT MAKES
PCI GEOMATICS
DIFFERENT

6. We provide…
Powerful and scalable
image processing
solutions that let you
quickly and efficiently
produce information
products from any type
of imagery
SCALABLE TO WE ARE UNMATCHED
ANY SIZE SENSOR AUTOMATED
PROJECT AGNOSTIC WORKFLOWS
BUILDING
HIGH SPEED ADVANCED
SOLUTIONS
MULTI FOR RADAR
CPU / GPU CAPABILITY
30 YEARS

7. WHICH SOLUTION IS RIGHT FOR YOU?
$1M
$500
Price
($000’s)
$200
$10
10 GB 100 GB 500 GB 1 - 5TB 5 - 10TB
Page 7

8. PCI – SAR technology development
 Canada has been an innovator in SAR data
acquisition and processing since the early
1980s – PCI has been involved since the
beginning
 PCI Geomatics participated in GlobeSAR
program, delivered training and software
 PCI Geomatics developed technology
through Canadian Government (SAR
Polarimetry Workstation)
 PCI Geomatics works with multi-sensor SAR
imagery
Page 8

9. SAR Sensor Support
 RADARSAT 1 & 2
 TerraSAR-X
 Cosmo-SkyMed,
 UAVSAR
 PALSAR
 ASAR
 ERS 1 & 2
Page 9

10. Generic SAR Capabilities
 Support for Single, Dual, Quad, Data
 Automatic Calibration*
 Automatic Geocoding*
 Speckle Filtering (many)
 Statistical & Analysis Capabilities
 Ortho-rectification, Integration and
Visualization with Optical Data
* If available
Page 10

11. Generic SAR Capabilities
 Supported Calibration Types
• Sigma,
• Beta,
• Gamma,
• None
 Multi-Channel Representations
• Scattering
• Covariance
• Coherence
• Kennaugh
Page 11

12. Advantages for applications
 Key Advantages of Commercial Radar Imagery
– Data collections are independent of lighting and cloud conditions
– Frequent imaging supports routine change detection
– Provides effective wide area (100 –500+ km swath) coverage
– A variety of information is contained in the return signal that can be
extracted
 Key Maritime Missions:
– Large Area Maritime Domain Awareness
– Efficient Tasking of Patrol Assets
– Monitoring Port Activity
 Key Terrestrial Missions:
– Change Detection
– Disaster Response
– DEM Generation
Page 12

13. Application examples
Change
Detection
Page 13

14. Change Detection Methods
1. Amplitude Change Detection
2. Coherence Change Detection
3. Polarimetric Analysis and Change
Detection
Page 14

15. 1. Amplitude Change Detection
 Different sensors / beam modes /
resolutions can be used in combination
 Revisit is more important in this case than
matching geometry
 Presence / absence of features readily
observed
Page 15

16. Change Detection Results
Phoenix Airport
Sunday May 4, 2008

17. Change Detection Results
Phoenix Airport
Weds. May 28, 2008

18. Detected Changes
Phoenix Airport
Change Map
May 4 , 2008
May 28, 2008

19. 2. Coherent Change Detection
 Measures phase differences in SAR signal
 Geometry must be matching (repeat pass)
 Multiple collections over same area from
different sensors/orbits can be combined
Page 19

20. Coherent Change Detection
Change in
Coherence (phase)
Image 1
Page 20

21. Coherent Change Detection
Change in
Coherence (phase)
Image 2
Acquired 11 min. later
Page 21

22. Coherent Change Detection
Loss of Coherence
is indicated by
Dark Colour
Note:
Loss of Coherence for Trees
Page 22

23. Cross Sensor Change Detection
 Sample CCD over Flevoland
 TerraSAR-X and RADARSAT-2
acquisitions
 Two sets of repeat pass collections
 PCI Technology used to achieve high
cross-sensor image registration
Page 23

24. Flevoland, May 07/2010
RADARSAT-2 Total
Power Page 24

25. Flevoland, May 07/2010
RADARSAT-2 Total
Power Page 25

26. Cross Sensor Change Detection
(May 04 - May 07, 2010)
Optical (Google Map™) TSX-1/RSAT-2 Change Map
Page 26

27. Cross Sensor Change Detection
(May 04 - May 07, 2010)
Target May 04 TSX-1/RSAT-2 Change Map
Page 27

28. Cross Sensor Change Detection
(May 04 - May 07, 2010)
No Target May 07 TSX-1/RSAT-2 Change Map
Page 28

29. Cross Sensor Change Detection
(May 04 - May 07, 2010)
No Target May 04 TSX-1/RSAT-2 Change Map
Page 29

30. Cross Sensor Change Detection
(May 04 - May 07, 2010)
TSX-1/RSAT-2 Change Map
Optical (Google Map™)
Page 30

31. Cross Sensor Change Detection
(May 04 - May 07, 2010)
Optical (Google Map™) TSX-1/RSAT-2 Change Map
Page 31

32. Application examples
Ship detection
(polarimetry)
Page 32

33. 3. Polarimetric Analysis and Change
Detection
 Basics of Polarimetry
 Polarimetric information for ship dectection
Page 33

34. Some Polarimetric Basics
V
For a single
polarization, the
return is
proportional to the
target cross
section.
H
For HH we would
get a return
indicated by red.
For VV it would be
blue.
So the amount of return we get depends on
target orientation and polarization
Page 34

35. Some Polarimetric Basics
V
For a single
polarization, the
return is
proportional to the
target cross
section.
For HH we would H
get a return
indicated by red.
For VV it would be
blue.
So the amount of return we get depends on
target orientation and polarization
Page 35

36. Some Polarimetric Basics
X Polarimetric radar
data provides full
scattering information
in the direction of the
line of sight
Y
Y X
We want to compare these targets.
Page 36

37. Some Polarimetric Basics
Polarimetric radar
data provides full
scattering information
in the direction of the
Y line of sight
Y H X
Y
X
H
We can do some fancy arithmetic and rotate the
scattering matrix until we get a maximum X and a
minimum Y.
Then we can compare their properties. Page 37

38. Non-polarimetric Parameters
Time 2001-02-30 12:34:56 GMT
Position: 12:01:21.58 N 34:14:43.37 W
Incidence Angle: 27.15°
Estimated Length: 226 m
Estimated Heading: 260°
Estimated Velocity: 9.70 m/s
Page 38

39. Polarimetric Processing Steps
 Ingest Full Polarimetric Data
 (Optionally) calibrate to σ
 Apply multi-channel speckle filter
 Decompose (Cloude-Pottier) image into (16) polarimetric classes
 Iterate (3-5 times) to enhance classification and remove outliers
 Exclude pixels from the largest class (which will be water)
 Generate land mask *
 Generate polarimetric parameters using FOCUS, SPW and SPTA
from remaining (non-masked) pixels
Page 39

40. Example Polarimetric Ship Analysis
Page 40

41. Polarimetric Information
Maximum of the degree of polarization: 0.7916655
Minimum of the degree of polarization: 0.09595539
Maximum of the completely polarized component: 2.520944
Minimum of the completely polarized component: 0.2940039
Orientation of Maximum Polarisation 70
Ellipticity of Maximum Polarisation -5
Maximum of the completely unpolarized component: 2.769960
Minimum of the completely unpolarized component: 0.6619406
Maximum of the scattered intensity: 3.210612
LL
Minimum of the scattered intensity: 2.850842
Coefficient of Variation: 0.1160221
Fractional Power: 0.7920792
HH VV
Pedestal Height 1.318336
HH / HV Ratio 4.014223
HH / HV Correlation 0.2035844 RR
HH / VV Ratio 0.9518262
Page 41
HH / VV Correlation 0.3857002

42. Polarimetric Signature Information
V
LL
5° Ellipticity
70° Orientation
H
VV
Maximum Return
V
LL
RR
Secondary
HH Return Max
Return
H
- 20° Orientation
Strong Secondary Return
Page 42
RR

43. Power Distribution
By Polarization
HH HV
VV
By Type
Double Diffuse
Surface
By Scatterer
Primary Secondary
Tertiary

44. Polarimetric Decompositions
Cloude-Pottier
Target Average % High % Medium % Low
Entropy 0.8480822 2.253302 76.30148 21.44522
Anisotropy 0.5064220 55.63326 44.36674
Alpha Angle 43.200062 27.50583 30.53613 41.95804
Touzi (ICTD)
Target Tilt Dominant Eigen Symmetric Symmetric Helicity
Angle Value Scattering Type Scattering Type (Symmetry)
(deg) Magnitude Phase (deg)
-27.432373 0.5600992 10.467688 -50.483246 5.841676
van Zyl
% Unclassified % Odd % Even % Diffuse
1.892744 48.264984 23.343849 26.498423
Page 44

45. van Zyl Decomposition
Radar Measurement Physical Meaning
Odd Number Bounce Flat Surface
Even Number Bounce Superstructure
Diffuse Scattering Complex / Random
Page 45

46. Symmetric Scattering Decomposition
Trihedral
(odd number of bounces)
Cylinder
(weak return in one direction)
Dipole
(no return in one direction)
Quarter Wave
(delay in second direction)
Dihedral
(even number of bounces)
Narrow Dihedral
(with one direction attenuated)
Page 46

47. Classification based upon Polarimetric
Signatures ?
1-5
6 - 10
11 - 15
16 - 20

48. Classification based upon Polarimetric
Signatures ?
1-5
6 - 10
11 - 15
16 - 20

49. Polarimetric Power Distribution Comparison
Polarization
Type
Scatterer
Page 49

50. Application examples
Digital Elevation
Extraction
Page 50

51. Multi-Channel Input
HH
HV
Span
VH
VV

52. Stereo DEMs
Find highest correlation within search window
R1
R2
Compute Stereo Intersection Generate DEM

53. Geometric Problem
Intermediate Angle
What the Radar
Sees

54. Geometric Problem
Shallow
Angle

55. Stereo DEMs
All or
Maximum Overlap
Next Pair
Image match based upon Power
Linear or Decibels
Image A Image B
No
Overlap, Look Direction
Angular Difference Suitable Pair ?
Downsample Image A Downsample Image B
to User Specs to Epipolar Image A
Spacing Affects DEM
Detail Level Extract Window Area Extract Search Area
Ignore Background Find Common Points
(No Data) Pixels
Stereo Intersection
Store Elevation
No
Last Pair ?
Blend Overlap Areas
Last, Average, High Score Arbitrate Values
Write Failed Value where Fill Gaps/Holes
“gaps” remain
Remove “buildings “ * Write Final DEM

56. Suggestions for Selection of Stereo Pairs
Selection of Stereo Image Pairs
 Candidate pair should have more than 50 % overlap
 Candidate pair should have nominally the same resolution
 Best results obtained from same-side (i.e., descending/descending
or ascending/ascending) image pairs
 Candidate pair should have matching polarizations
 Large incident angle (i.e., S7 ) are preferable (to minimize terrain
displacement effects)
 The larger the difference between incident angles, the greater the
parallax in the stereo pair (recommend 5 - 25 angular difference)
 Opposite-side (i.e., ascending/descending) image pairs only
recommended for very low relief areas; with similar tonal
characteristics

57. Application examples
Flood Monitoring
Page 57

58. SAR derived real time flooding
information – Manitoba, Canada

59. RADARSAT-2 acquisition
April 15, 2011 - 00:11 UTC
Red River
© Her Majesty the Queen in Right of Canada, Department of Natural Resources. All rights reserved.

60. RADARSAT-2 acquisition
April 18, 2011 - 12:32 UTC
Red River
© Her Majesty the Queen in Right of Canada, Department of Natural Resources. All rights reserved.

61. RADARSAT-1 acquisition
April 20, 2011 - 00:15 UTC
Red River
© Her Majesty the Queen in Right of Canada, Department of Natural Resources. All rights reserved.

62. RADARSAT-2 acquisition
April 22, 2011 - 00:07 UTC
Red River
© Her Majesty the Queen in Right of Canada, Department of Natural Resources. All rights reserved.

63. RADARSAT-2 acquisition
April 25, 2011 - 12:27 UTC
Red River
© Her Majesty the Queen in Right of Canada, Department of Natural Resources. All rights reserved.

64. RADARSAT-2 acquisition
April 21, 2011 - 00:36 UTC
Assiniboine River
Approximate location of air photo
© Her Majesty the Queen in Right of Canada, Department of Natural Resources. All rights reserved.

65. Assiniboine River April 20, 2011 at PTH 21
near previous Radarsat image

66. Application examples
Ocean Features
Page 66

67. Wind Speed Analysis
Steps:
 #1: Convert to calibrated data (SARINGEST)
 #2: Boxcar Filter (19 x 19)
 #3: Convert filtered HV data to decibel
 #4: If HV data ( < -21 dB) apply Paris Vachon
algorithm to generate Windspeed in m/s.
Purple = 10 m/s to red = 16 m/s.
Page 67

68. RADARSAT HV in dB
Page 68

69. Derived Windspeed
Page 69

70. Page 70

71. Summary of PCI Capabilities
Software / scalable Experience/ know-how
 Geomatica Radar Suite  Dedicated development
www.pcigeomatics.com/sar team
 Includes SPW and Target  Senior SAR scientist on
Analysis
 Ingest, correct Multi-sensor
staff
SAR data  30 years of experience
 SAR training available
 SAR for GXL
 Custom implementation of
SAR analysis for large
volume processing
Page 71

72. Contact PCI Geomatics
TORONTO GATINEAU
50 West Wilmot 490 St-Joseph Boulevard www.pcigeomatics.com
Richmond Hill, ON Gatineau, QC
Canada, M4B 1M5 Canada, J8Y 3Y6 info@pcigeomatics.com
Phone: (905) 764-0614 Phone: (819) 770-0022
Fax: (905) 764-9064 Fax: (905) 770-0098
@pcigeomatics
www.pcigeomatics.tv
www.facebook.com/pcigeomatics
www.linkedin.com/company/pci-geomatics
www.flickr.com/pcigeomatics
Page 72